Volume 12 • Issue 1 • PP: 01-14 • 2025
Optimizing Business Process through Fault-Tolerant Scheduling in Cloud Environments: A Comparative Study
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The fault tolerance study carried out in this research explores Bidirectional Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GAN) to improve cloud computing dependability and functionality. Being an integral part of the rage for business operations, cloud-computing fundamentals of resource provisioning and fault tolerance have a bearing on the overall cost-dynamics, ROI and OpEx. Reliability covers such issues as hardware failures, configuration problems and other network issues that may have financial implications and even lead to revenue loss, and failure to meet service level agreement (SLA). The work develops a novel GAN-BiLSTM model for the accurate prediction of faults and the enhancement of recovery management, resulting in resource efficiency and cost of capital reduction (CapEx). Evaluation criteria involve deadline guarantee ratio, average task delay, and system scalability, confirming that the proposed model has better financial performance than DPSO and ANFIS. Cutting wastage of resources and increasing energy capacity in a system, the model displays attractive cost reduction and operating effectiveness for cloud service providers. In the simulation, important results of the model are demonstrated in the business continuity, financial risk reduction as well as maintaining accurate and resourceful service in high demand situations. All these developments have placed the fault tolerant systems powered by machine learning as indispensable instruments that can also enhance profitability, resources utilisation and sustainable competitiveness in the cloud computing business.
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